Check this 90-second animation to know what is Digital Asset Management (DAM) system and how it works for businesses and organizations.
http://pics.io/digital-asset-management
Digital asset management definition, according to Wikipedia, tells that this notion consists of management tasks and decisions surrounding the ingestion, annotation, cataloguing, storage, retrieval and distribution of digital assets. In simple words digital asset management solutions or systems allow to keep, organize, retrieve and use different media assets. But what is a digital asset? Digital assets are pictures, photos, drawings, video, audio documents, etc. There are plenty of digital asset management vendors on the market who propose their systems and pics.io is one of the most progressive and modern among them.

A multi-tasking, multi-user business computer from the late 1970's - early 1980's. If you can provide a good home for it, such as at a college or museum, let me know and maybe we can work something out. woodywrkng@gmail.com

published:11 Jun 2015

views:4640

published:25 Nov 2016

views:46

Joe Kava, VP of Google's Data Center Operations, gives a tour inside a Google data center, and shares details about the security, sustainability and the core architecture of Google's infrastructure.

published:17 Dec 2014

views:8520535

Authors:
Liang Zhang, LinkedIn CorporationBenjamin Le, LinkedIn Corporation
NadiaFawaz, LinkedIn Corporation
Ganesh Venkataraman, LinkedIn Corporation
Abstract:
Deep learning has been widely successful in solving complex tasks such as image recognition (ImageNet), speech recognition, machine translation, etc. In the area of personalized recommender systems, deep learning has started showing promising advances in recent years. The key to success of deep learning in personalized recommender systems is its ability to learn distributed representations of users’ and items’ attributes in low dimensional dense vector space and combine these to recommend relevant items to users. To address scalability, the implementation of a recommendation system at web scale often leverages components from information retrieval systems, such as inverted indexes where a query is constructed from a user’s attribute and context, learning to rank techniques. Additionally, it relies on machine learning models to predict the relevance of items, such as collaborative filtering. In this tutorial, we present ways to leverage deep learning towards improving recommender system. The tutorial is divided into four parts: (1) In the first part, we will present an overview of concepts in deep learning which are pertinent to recommender systems including sequence modeling, word embedding and named entity recognition. (2) In the second part, we will present how these fundamental building blocks can be used to improve a recommender system at scale. (3) The third part presents a few case studies from large scale recommender systems at LinkedIn and some of the challenges we faced while getting deep learning to work in production.
Link to tutorial: https://engineering.linkedin.com/data/publications/kdd-2017/deep-learning-tutorial
More on http://www.kdd.org/kdd2017/
KDD2017 Conference is published on http://videolectures.net/

published:17 Nov 2017

views:728

In this video, we build our own recommendation system that suggests movies a user would like in 40 lines of Python using the LightFM recommendation library. I start off by talking about why we need recommendation systems, then we dive straight into installing our dependencies and writing our script.
The coding challenge for this video is here:
https://github.com/llSourcell/recommender_system_challenge
The winner of last weeks coding challenge (Rohan Verma):
https://twitter-sentiment-csv.herokuapp.com/
https://t.co/4eg8UdlaSB
The runner up (Arnaud Delauney):
https://github.com/arnauddelaunay/twitter_sentiment_challenge
I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/
The LightFM Python Library:
https://github.com/lyst/lightfm/tree/master/lightfm
Some great learning resources on recommender systems:
http://blogs.gartner.com/martin-kihn/how-to-build-a-recommender-system-in-python/
https://www.analyticsvidhya.com/blog/2015/08/beginners-guide-learn-content-based-recommender-systems/
http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/
http://blog.manugarri.com/a-short-introduction-to-recommendation-systems/
Best book to become a Python God:
https://learnpythonthehardway.org/
Please share this video, like, comment and subscribe! That's what keeps me going.
Please support me on Patreon!:
https://www.patreon.com/user?u=3191693
Follow me:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

published:22 Oct 2016

views:66754

What exactly is an API? Finally learn for yourself in this helpful video from MuleSoft, the API experts. https://www.mulesoft.com/platform/api
The textbook definition goes something like this:
“An application programming interface (API) is a set of routines, protocols, and tools for building software applications. An API expresses a software component in terms of its operations, inputs, outputs, and underlying types. An API defines functionalities that are independent of their respective implementations, which allows definitions and implementations to vary without compromising each other. A good API makes it easier to develop a program by providing all the building blocks.
APIs often come in the form of a library that includes specifications for routines, data structures, object classes, and variables. In other cases, notably SOAP and REST services, an API is simply a specification of remote calls exposed to the API consumers.
An API specification can take many forms, including an International Standard, such as POSIX, vendor documentation, such as the Microsoft Windows API, or the libraries of a programming language, e.g., the Standard Template Library in C++ or the Java APIs.
An API differs from an application binary interface (ABI) in that an API is source code-based while an ABI is a binary interface. For instance POSIX is an API, while the Linux Standard Base provides an ABI”.
To speak plainly, an API is the messenger that runs and delivers your request to the provider you’re requesting it from, and then delivers the response back to you.
To give you a familiar example, think of an API as a waiter in a restaurant.
Imagine you’re sitting at the table with a menu of choices to order from, and the kitchen is the provider who will fulfill your order.
What’s missing is the critical link to communicate your order to the kitchen and deliver your food back to your table.
That’s where the waiter (or API) comes in. ”AHEM”
The waiter takes your order, delivers it to the kitchen, and then delivers the food (or response) back to you. (Hopefully without letting your order crash if designed correctly)
Now that we’ve whetted your appetite, let’s apply this to a real API example. In keeping with our theme, let’s book a flight to a culinary capital – Paris.
You’re probably familiar with the process of searching for airline flights online. Just like at a restaurant, you have a menu of options to choose from ( a dropdown menu in this case). You choose a departure city and date, a return city and date, cabin class, and other variables (like meal or seating, baggage or pet requests)
In order to book your flight, you interact with the airline’s website to access the airline’s database to see if any seats are available on those dates, and what the cost might be based on certain variables.
But, what if you are not using the airline’s website, which has direct access to the information? What if you are using online travel service that aggregates information from many different airlines? Just like a human interacts with the airline’s website to get that information, an application interacts with the airline’s API.
The API is the interface that, like your helpful waiter, runs and and delivers the data from that online travel service to the airline’s systems over the Internet.
It also then takes the airline’s response to your request and delivers right back to the online travel service .
And through each step of the process it facilitates that interaction between the travel service and the airline’s systems - from seat selection to payment and booking.
So now you can see that it’s APIs that make it possible for us all to use travel sites. They interface with with airlines’ APIs to gather information in order to present options back to us
The same goes for all interactions between applications, data and devices - they all have API’s that allow computers to operate them, and that's what ultimately creates connectivity.
API’s provide a standard way of accessing any application, data or device whether it is shopping from your phone, or accessing cloud applications at work.
So, whenever you think of an API, just think of it as your waiter running back and forth between applications, databases and devices to deliver data and create the connectivity that puts the world at our fingertips. And whenever you think of creating an API, think MuleSoft.

published:19 Jun 2015

views:1308277

Optimal has developed a bespoke machine vision system for the real-time 100 percent inspection of a thin film product used in the manufacture of electronic components. The new system builds on Optimal's 26 years of systems integration experience and makes use of the company's synTI® integrated Print and Inspect system software.
Optimal’s customer for the new system wanted to replace its previous sample-based quality assurance regime with a more detailed 100 percent inspection approach, but it was concerned that its high manufacturing rates and detailed inspection requirements would make the required level of speed and accuracy difficult to achieve. The material travels at relatively high speed, and the inspection system needs to spot tiny defects in a web 900mm wide, as well as recording very accurate dimensional measurements.
Optimal tackled the problem with a system that uses three, synchronized high resolution, high speed contact image sensors (CIS) installed on the customer’s production line between the manufacture of the film material and downstream slitting and packaging operations. One camera inspects the top of the web of material; the other two are focused on the underside.
The inspection system checks for defects in bands of dark and light coloured coatings on the film, and measures the precise width of the coloured bands. The inspection data is processed by the synTI® software and displayed in real time on the production line, so that operators can check the performance of their upstream processes and make any adjustments or interventions necessary to keep quality within the required tolerance limits.
A summary of the inspection information is also sent automatically to label printing equipment to be added to every batch of material prior to dispatch, and all data is stored in an online database to permit later management review.
With three high resolution sensors each running at up to 10kHz frame rate, the system can generate and manage up to a Gigabit of data every second, although the actual stored data is not that high as the software is able to process the raw data into more a more manageable format.
“Thanks to advances in technology like high speed cameras, high speed communications and powerful processors, our synTI® system can now manage the process in real time.’ says GeoffNorwood, ApplicationsEngineer for vision systems at Optimal. ‘The system means our customer can now inspect 100 percent of their product, 100 percent of the time.”
The synTI® software used to run the film inspection system runs on four powerful PCs which are housed with the rest of the control hardware in a racked cabinet, also built-up and supplied by Optimal.
This combination of highly capable sensors, fast data transfer and powerful processing capabilities is allowing Optimal to solve an increasing number of challenging inspection problems for its customers “Modern cameras can do measurements, or use advanced tools like feature recognition or optical character recognition too,” notes Norwood.
“In this case, we were using a small number of high resolution cameras, but in other examples we might use larger networks of simpler devices. synTI® will interface with a large number of measurement and output devices from cameras to check weigh scales, labellers or laser marking systems.”
While Optimal is often asked to develop systems for continuous manufacturing applications like this example, its skills are also increasingly being used in high speed discrete manufacturing, where they have been applied to a range of tasks, including the detection of marks, stains and defects, non contact measurement and the identification of products by vision, character recognition or code reading tools.
“The ability to process high volumes of data in real time opens up a new world of possibilities in machine vision,” concludes Geoff Norwood. “From 3D data acquisition to the use of image processing on ultra high speed production lines.”
CompanyContact
Optimal Industrial AutomationLimited : Martin Gadsby
Tel: +44 (0) 1454 333222 Fax: +44 (0) 1454 322 240
Web: www.optimal-ltd.co.uk
Email: mgadsby@optimal-ltd.co.uk

Get the webinar replay and slide deck here: https://www.owox.com/c/h3
When you have your data collected in a number of systems — ERP, CRM, advertising services, price intelligence services, etc. — piecing it together manually is often a tedious and error-prone task.
We will look at specific examples of system integrations, and give you examples of reports and charts which you can create by combining data.
Join the webinar and find out:
1. What are the difficulties in combining data from multiple services.
2. How to combine data into a single system using DataVirtuality and OWOX BI https://www.owox.com/c/fg
3. Examples of imports and exports to and from Google BigQuery.
4. Examples of informative reports and charts based on complete data from multiple sources.
The webinar will be useful to:
Data analysts and technical experts who are looking to save time by automating manual processes.
More webinars about Google services best practices for Ecommerce businesses: https://www.owox.com/c/g2

System

A system is a set of interacting or interdependent component parts forming a complex/intricate whole. Every system is delineated by its spatial and temporal boundaries, surrounded and influenced by its environment, described by its structure and purpose and expressed in its functioning.

The term system may also refer to a set of rules that governs structure and/or behavior. Alternatively, and usually in the context of complex social systems, the term is used to describe the set of rules that govern structure and/or behavior.

Etymology

The term "system" comes from the Latin word systēma, in turn from Greekσύστημαsystēma: "whole compounded of several parts or members, system", literary "composition".

History

"System" means "something to look at". You must have a very high visual gradient to have systematization. In philosophy, before Descartes, there was no "system". Plato had no "system". Aristotle had no "system".

In the 19th century the French physicist Nicolas Léonard Sadi Carnot, who studied thermodynamics, pioneered the development of the concept of a "system" in the natural sciences. In 1824 he studied the system which he called the working substance (typically a body of water vapor) in steam engines, in regards to the system's ability to do work when heat is applied to it. The working substance could be put in contact with either a boiler, a cold reservoir (a stream of cold water), or a piston (to which the working body could do work by pushing on it). In 1850, the German physicist Rudolf Clausius generalized this picture to include the concept of the surroundings and began to use the term "working body" when referring to the system.

Image

An image (from Latin:imago) is an artifact that depicts visual perception, for example a two-dimensionalpicture, that has a similar appearance to some subject—usually a physical object or a person, thus providing a depiction of it.

A volatile image is one that exists only for a short period of time. This may be a reflection of an object by a mirror, a projection of a camera obscura, or a scene displayed on a cathode ray tube. A fixed image, also called a hard copy, is one that has been recorded on a material object, such as paper or textile by photography or any other digital process.

Raw data, i.e. unprocessed data, is a collection of numbers, characters; data processing commonly occurs by stages, and the "processed data" from one stage may be considered the "raw data" of the next. Field data is raw data that is collected in an uncontrolled in situ environment. Experimental data is data that is generated within the context of a scientific investigation by observation and recording.

The Latin word "data" is the plural of "datum", and still may be used as a plural noun in this sense. Nowadays, though, "data" is most commonly used in the singular, as a mass noun (like "information", "sand" or "rain").

Deep learning

Deep learning (deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures, or otherwise composed of multiple non-linear transformations.

Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc. Some representations make it easier to learn tasks (e.g., face recognition or facial expression recognition) from examples. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervisedfeature learning and hierarchical feature extraction.

Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain.

Application programming interface

An API expresses a software component in terms of its operations, inputs, outputs, and underlying types, defining functionalities that are independent of their respective implementations, which allows definitions and implementations to vary without compromising the interface. A good API makes it easier to develop a program by providing all the building blocks, which are then put together by the programmer.

An API may be for a web-based system, operating system, or database system, and it provides facilities to develop applications for that system using a given programming language. As an example, a programmer who develops apps for Android may use an Android API to interact with hardware, like the front camera of an Android-based device.

In addition to accessing databases or computer hardware like hard disk drives or video cards, an API can ease the work of programming GUI components. For example, an API can facilitate integration of new features into existing applications (a so-called "plug-in API"). An API can also assist otherwise distinct applications with sharing data, which can help to integrate and enhance the functionalities of the applications.

Digital Asset Management Explained (Animation)

Check this 90-second animation to know what is Digital Asset Management (DAM) system and how it works for businesses and organizations.
http://pics.io/digital-asset-management
Digital asset management definition, according to Wikipedia, tells that this notion consists of management tasks and decisions surrounding the ingestion, annotation, cataloguing, storage, retrieval and distribution of digital assets. In simple words digital asset management solutions or systems allow to keep, organize, retrieve and use different media assets. But what is a digital asset? Digital assets are pictures, photos, drawings, video, audio documents, etc. There are plenty of digital asset management vendors on the market who propose their systems and pics.io is one of the most progressive and modern among them.

Triad Systems Control Data 9427H 14" disc drive

A multi-tasking, multi-user business computer from the late 1970's - early 1980's. If you can provide a good home for it, such as at a college or museum, let me know and maybe we can work something out. woodywrkng@gmail.com

0:19

Download Data Mining in Biomedical Imaging Signaling and Systems Pdf

Download Data Mining in Biomedical Imaging Signaling and Systems Pdf

Download Data Mining in Biomedical Imaging Signaling and Systems Pdf

5:28

Inside a Google data center

Inside a Google data center

Inside a Google data center

Joe Kava, VP of Google's Data Center Operations, gives a tour inside a Google data center, and shares details about the security, sustainability and the core architecture of Google's infrastructure.

1:52:54

Deep Learning for Personalized Search and Recommender Systems part 1

Deep Learning for Personalized Search and Recommender Systems part 1

Deep Learning for Personalized Search and Recommender Systems part 1

Authors:
Liang Zhang, LinkedIn CorporationBenjamin Le, LinkedIn Corporation
NadiaFawaz, LinkedIn Corporation
Ganesh Venkataraman, LinkedIn Corporation
Abstract:
Deep learning has been widely successful in solving complex tasks such as image recognition (ImageNet), speech recognition, machine translation, etc. In the area of personalized recommender systems, deep learning has started showing promising advances in recent years. The key to success of deep learning in personalized recommender systems is its ability to learn distributed representations of users’ and items’ attributes in low dimensional dense vector space and combine these to recommend relevant items to users. To address scalability, the implementation of a recommendation system at web scale often leverages components from information retrieval systems, such as inverted indexes where a query is constructed from a user’s attribute and context, learning to rank techniques. Additionally, it relies on machine learning models to predict the relevance of items, such as collaborative filtering. In this tutorial, we present ways to leverage deep learning towards improving recommender system. The tutorial is divided into four parts: (1) In the first part, we will present an overview of concepts in deep learning which are pertinent to recommender systems including sequence modeling, word embedding and named entity recognition. (2) In the second part, we will present how these fundamental building blocks can be used to improve a recommender system at scale. (3) The third part presents a few case studies from large scale recommender systems at LinkedIn and some of the challenges we faced while getting deep learning to work in production.
Link to tutorial: https://engineering.linkedin.com/data/publications/kdd-2017/deep-learning-tutorial
More on http://www.kdd.org/kdd2017/
KDD2017 Conference is published on http://videolectures.net/

6:57

Recommendation Systems - Learn Python for Data Science #3

Recommendation Systems - Learn Python for Data Science #3

Recommendation Systems - Learn Python for Data Science #3

In this video, we build our own recommendation system that suggests movies a user would like in 40 lines of Python using the LightFM recommendation library. I start off by talking about why we need recommendation systems, then we dive straight into installing our dependencies and writing our script.
The coding challenge for this video is here:
https://github.com/llSourcell/recommender_system_challenge
The winner of last weeks coding challenge (Rohan Verma):
https://twitter-sentiment-csv.herokuapp.com/
https://t.co/4eg8UdlaSB
The runner up (Arnaud Delauney):
https://github.com/arnauddelaunay/twitter_sentiment_challenge
I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/
The LightFM Python Library:
https://github.com/lyst/lightfm/tree/master/lightfm
Some great learning resources on recommender systems:
http://blogs.gartner.com/martin-kihn/how-to-build-a-recommender-system-in-python/
https://www.analyticsvidhya.com/blog/2015/08/beginners-guide-learn-content-based-recommender-systems/
http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/
http://blog.manugarri.com/a-short-introduction-to-recommendation-systems/
Best book to become a Python God:
https://learnpythonthehardway.org/
Please share this video, like, comment and subscribe! That's what keeps me going.
Please support me on Patreon!:
https://www.patreon.com/user?u=3191693
Follow me:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

3:25

What is an API?

What is an API?

What is an API?

What exactly is an API? Finally learn for yourself in this helpful video from MuleSoft, the API experts. https://www.mulesoft.com/platform/api
The textbook definition goes something like this:
“An application programming interface (API) is a set of routines, protocols, and tools for building software applications. An API expresses a software component in terms of its operations, inputs, outputs, and underlying types. An API defines functionalities that are independent of their respective implementations, which allows definitions and implementations to vary without compromising each other. A good API makes it easier to develop a program by providing all the building blocks.
APIs often come in the form of a library that includes specifications for routines, data structures, object classes, and variables. In other cases, notably SOAP and REST services, an API is simply a specification of remote calls exposed to the API consumers.
An API specification can take many forms, including an International Standard, such as POSIX, vendor documentation, such as the Microsoft Windows API, or the libraries of a programming language, e.g., the Standard Template Library in C++ or the Java APIs.
An API differs from an application binary interface (ABI) in that an API is source code-based while an ABI is a binary interface. For instance POSIX is an API, while the Linux Standard Base provides an ABI”.
To speak plainly, an API is the messenger that runs and delivers your request to the provider you’re requesting it from, and then delivers the response back to you.
To give you a familiar example, think of an API as a waiter in a restaurant.
Imagine you’re sitting at the table with a menu of choices to order from, and the kitchen is the provider who will fulfill your order.
What’s missing is the critical link to communicate your order to the kitchen and deliver your food back to your table.
That’s where the waiter (or API) comes in. ”AHEM”
The waiter takes your order, delivers it to the kitchen, and then delivers the food (or response) back to you. (Hopefully without letting your order crash if designed correctly)
Now that we’ve whetted your appetite, let’s apply this to a real API example. In keeping with our theme, let’s book a flight to a culinary capital – Paris.
You’re probably familiar with the process of searching for airline flights online. Just like at a restaurant, you have a menu of options to choose from ( a dropdown menu in this case). You choose a departure city and date, a return city and date, cabin class, and other variables (like meal or seating, baggage or pet requests)
In order to book your flight, you interact with the airline’s website to access the airline’s database to see if any seats are available on those dates, and what the cost might be based on certain variables.
But, what if you are not using the airline’s website, which has direct access to the information? What if you are using online travel service that aggregates information from many different airlines? Just like a human interacts with the airline’s website to get that information, an application interacts with the airline’s API.
The API is the interface that, like your helpful waiter, runs and and delivers the data from that online travel service to the airline’s systems over the Internet.
It also then takes the airline’s response to your request and delivers right back to the online travel service .
And through each step of the process it facilitates that interaction between the travel service and the airline’s systems - from seat selection to payment and booking.
So now you can see that it’s APIs that make it possible for us all to use travel sites. They interface with with airlines’ APIs to gather information in order to present options back to us
The same goes for all interactions between applications, data and devices - they all have API’s that allow computers to operate them, and that's what ultimately creates connectivity.
API’s provide a standard way of accessing any application, data or device whether it is shopping from your phone, or accessing cloud applications at work.
So, whenever you think of an API, just think of it as your waiter running back and forth between applications, databases and devices to deliver data and create the connectivity that puts the world at our fingertips. And whenever you think of creating an API, think MuleSoft.

4:14

Managing big data vision systems

Managing big data vision systems

Managing big data vision systems

Optimal has developed a bespoke machine vision system for the real-time 100 percent inspection of a thin film product used in the manufacture of electronic components. The new system builds on Optimal's 26 years of systems integration experience and makes use of the company's synTI® integrated Print and Inspect system software.
Optimal’s customer for the new system wanted to replace its previous sample-based quality assurance regime with a more detailed 100 percent inspection approach, but it was concerned that its high manufacturing rates and detailed inspection requirements would make the required level of speed and accuracy difficult to achieve. The material travels at relatively high speed, and the inspection system needs to spot tiny defects in a web 900mm wide, as well as recording very accurate dimensional measurements.
Optimal tackled the problem with a system that uses three, synchronized high resolution, high speed contact image sensors (CIS) installed on the customer’s production line between the manufacture of the film material and downstream slitting and packaging operations. One camera inspects the top of the web of material; the other two are focused on the underside.
The inspection system checks for defects in bands of dark and light coloured coatings on the film, and measures the precise width of the coloured bands. The inspection data is processed by the synTI® software and displayed in real time on the production line, so that operators can check the performance of their upstream processes and make any adjustments or interventions necessary to keep quality within the required tolerance limits.
A summary of the inspection information is also sent automatically to label printing equipment to be added to every batch of material prior to dispatch, and all data is stored in an online database to permit later management review.
With three high resolution sensors each running at up to 10kHz frame rate, the system can generate and manage up to a Gigabit of data every second, although the actual stored data is not that high as the software is able to process the raw data into more a more manageable format.
“Thanks to advances in technology like high speed cameras, high speed communications and powerful processors, our synTI® system can now manage the process in real time.’ says GeoffNorwood, ApplicationsEngineer for vision systems at Optimal. ‘The system means our customer can now inspect 100 percent of their product, 100 percent of the time.”
The synTI® software used to run the film inspection system runs on four powerful PCs which are housed with the rest of the control hardware in a racked cabinet, also built-up and supplied by Optimal.
This combination of highly capable sensors, fast data transfer and powerful processing capabilities is allowing Optimal to solve an increasing number of challenging inspection problems for its customers “Modern cameras can do measurements, or use advanced tools like feature recognition or optical character recognition too,” notes Norwood.
“In this case, we were using a small number of high resolution cameras, but in other examples we might use larger networks of simpler devices. synTI® will interface with a large number of measurement and output devices from cameras to check weigh scales, labellers or laser marking systems.”
While Optimal is often asked to develop systems for continuous manufacturing applications like this example, its skills are also increasingly being used in high speed discrete manufacturing, where they have been applied to a range of tasks, including the detection of marks, stains and defects, non contact measurement and the identification of products by vision, character recognition or code reading tools.
“The ability to process high volumes of data in real time opens up a new world of possibilities in machine vision,” concludes Geoff Norwood. “From 3D data acquisition to the use of image processing on ultra high speed production lines.”
CompanyContact
Optimal Industrial AutomationLimited : Martin Gadsby
Tel: +44 (0) 1454 333222 Fax: +44 (0) 1454 322 240
Web: www.optimal-ltd.co.uk
Email: mgadsby@optimal-ltd.co.uk

How to combine data from your website, CRM, ERP and other systems

Get the webinar replay and slide deck here: https://www.owox.com/c/h3
When you have your data collected in a number of systems — ERP, CRM, advertising services, price intelligence services, etc. — piecing it together manually is often a tedious and error-prone task.
We will look at specific examples of system integrations, and give you examples of reports and charts which you can create by combining data.
Join the webinar and find out:
1. What are the difficulties in combining data from multiple services.
2. How to combine data into a single system using DataVirtuality and OWOX BI https://www.owox.com/c/fg
3. Examples of imports and exports to and from Google BigQuery.
4. Examples of informative reports and charts based on complete data from multiple sources.
The webinar will be useful to:
Data analysts and technical experts who are looking to save time by automating manual processes.
More webinars about Google services best practices for Ecommerce businesses: https://www.owox.com/c/g2

The CaptiView image injection module displays data from Image Guided Surgery (IGS) systems directly in the eyepieces of a Leica microscope during neurosurgery. Surgeons therefore have the high-contrast, high-resolution data they need to make confident surgical decisions - without taking their eyes off the patient.
In this video you can see data from the neuronavigation software Cranial 3.1 from BrainLab overlaid onto the live surgical image during neurosurgery. The video is courtesy of Mount Sinai Health System, New York.
Get full details about CaptiView image injection:
http://www.leica-microsystems.com/products/surgical-microscopes/neurosurgery-spine/details/product/captiview/
Read more about surgical image injection and IGS in our ScienceLab portal:
http://www.leica-microsystems.com/science-lab/topics/surgical-microscopy/

Digital Asset Management Explained (Animation)

Check this 90-second animation to know what is Digital Asset Management (DAM) system and how it works for businesses and organizations.
http://pics.io/digital-asset-management
Digital asset management definition, according to Wikipedia, tells that this notion consists of management tasks and decisions surrounding the ingestion, annotation, cataloguing, storage, retrieval and distribution of digital assets. In simple words digital asset management solutions or systems allow to keep, organize, retrieve and use different media assets. But what is a digital asset? Digital assets are pictures, photos, drawings, video, audio documents, etc. There are plenty of digital asset management vendors on the market who propose their systems and pics.io is one of the most progressive and modern among t...

Lecture - 10 Data Acquisition Systems

Triad Systems Control Data 9427H 14" disc drive

A multi-tasking, multi-user business computer from the late 1970's - early 1980's. If you can provide a good home for it, such as at a college or museum, let me know and maybe we can work something out. woodywrkng@gmail.com

published: 11 Jun 2015

Download Data Mining in Biomedical Imaging Signaling and Systems Pdf

published: 25 Nov 2016

Inside a Google data center

Joe Kava, VP of Google's Data Center Operations, gives a tour inside a Google data center, and shares details about the security, sustainability and the core architecture of Google's infrastructure.

published: 17 Dec 2014

Deep Learning for Personalized Search and Recommender Systems part 1

Authors:
Liang Zhang, LinkedIn CorporationBenjamin Le, LinkedIn Corporation
NadiaFawaz, LinkedIn Corporation
Ganesh Venkataraman, LinkedIn Corporation
Abstract:
Deep learning has been widely successful in solving complex tasks such as image recognition (ImageNet), speech recognition, machine translation, etc. In the area of personalized recommender systems, deep learning has started showing promising advances in recent years. The key to success of deep learning in personalized recommender systems is its ability to learn distributed representations of users’ and items’ attributes in low dimensional dense vector space and combine these to recommend relevant items to users. To address scalability, the implementation of a recommendation system at web scale often leverages components fro...

published: 17 Nov 2017

Recommendation Systems - Learn Python for Data Science #3

In this video, we build our own recommendation system that suggests movies a user would like in 40 lines of Python using the LightFM recommendation library. I start off by talking about why we need recommendation systems, then we dive straight into installing our dependencies and writing our script.
The coding challenge for this video is here:
https://github.com/llSourcell/recommender_system_challenge
The winner of last weeks coding challenge (Rohan Verma):
https://twitter-sentiment-csv.herokuapp.com/
https://t.co/4eg8UdlaSB
The runner up (Arnaud Delauney):
https://github.com/arnauddelaunay/twitter_sentiment_challenge
I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/
The LightFM Python Library:
https://github.com/lyst/lightfm/tree/master/lightfm
Some ...

published: 22 Oct 2016

What is an API?

What exactly is an API? Finally learn for yourself in this helpful video from MuleSoft, the API experts. https://www.mulesoft.com/platform/api
The textbook definition goes something like this:
“An application programming interface (API) is a set of routines, protocols, and tools for building software applications. An API expresses a software component in terms of its operations, inputs, outputs, and underlying types. An API defines functionalities that are independent of their respective implementations, which allows definitions and implementations to vary without compromising each other. A good API makes it easier to develop a program by providing all the building blocks.
APIs often come in the form of a library that includes specifications for routines, data structures, object clas...

published: 19 Jun 2015

Managing big data vision systems

Optimal has developed a bespoke machine vision system for the real-time 100 percent inspection of a thin film product used in the manufacture of electronic components. The new system builds on Optimal's 26 years of systems integration experience and makes use of the company's synTI® integrated Print and Inspect system software.
Optimal’s customer for the new system wanted to replace its previous sample-based quality assurance regime with a more detailed 100 percent inspection approach, but it was concerned that its high manufacturing rates and detailed inspection requirements would make the required level of speed and accuracy difficult to achieve. The material travels at relatively high speed, and the inspection system needs to spot tiny defects in a web 900mm wide, as well as recording ...

published: 05 Jan 2015

Deep Learning: Intelligence from Big Data

Deep Learning: Intelligence from Big Data
Tue Sep 16, 2014 6:00 pm - 8:30 pm
Stanford Graduate School of BusinessKnightManagementCenter – CemexAuditorium
641 Knight Way, Stanford, CA
A machine learning approach inspired by the human brain, Deep Learning is taking many industries by storm. Empowered by the latest generation of commodity computing, Deep Learning begins to derive significant value from Big Data. It has already radically improved the computer’s ability to recognize speech and identify objects in images, two fundamental hallmarks of human intelligence.
Industry giants such as Google, Facebook, and Baidu have acquired most of the dominant players in this space to improve their product offerings. At the same time, startup entrepreneurs are creating a new paradigm, Intellige...

published: 31 Oct 2014

How to combine data from your website, CRM, ERP and other systems

Get the webinar replay and slide deck here: https://www.owox.com/c/h3
When you have your data collected in a number of systems — ERP, CRM, advertising services, price intelligence services, etc. — piecing it together manually is often a tedious and error-prone task.
We will look at specific examples of system integrations, and give you examples of reports and charts which you can create by combining data.
Join the webinar and find out:
1. What are the difficulties in combining data from multiple services.
2. How to combine data into a single system using DataVirtuality and OWOX BI https://www.owox.com/c/fg
3. Examples of imports and exports to and from Google BigQuery.
4. Examples of informative reports and charts based on complete data from multiple sources.
The webinar will be useful ...

published: 29 Mar 2017

Management Information Systems: Data and Databases

published: 02 Dec 2016

Data explains complex systems.

The CaptiView image injection module displays data from Image Guided Surgery (IGS) systems directly in the eyepieces of a Leica microscope during neurosurgery. Surgeons therefore have the high-contrast, high-resolution data they need to make confident surgical decisions - without taking their eyes off the patient.
In this video you can see data from the neuronavigation software Cranial 3.1 from BrainLab overlaid onto the live surgical image during neurosurgery. The video is courtesy of Mount Sinai Health System, New York.
Get full details about CaptiView image injection:
http://www.leica-microsystems.com/products/surgical-microscopes/neurosurgery-spine/details/product/captiview/
Read more about surgical image injection and IGS in our ScienceLab portal:
http://www.leica-microsystems.co...

Check this 90-second animation to know what is Digital Asset Management (DAM) system and how it works for businesses and organizations.
http://pics.io/digital-asset-management
Digital asset management definition, according to Wikipedia, tells that this notion consists of management tasks and decisions surrounding the ingestion, annotation, cataloguing, storage, retrieval and distribution of digital assets. In simple words digital asset management solutions or systems allow to keep, organize, retrieve and use different media assets. But what is a digital asset? Digital assets are pictures, photos, drawings, video, audio documents, etc. There are plenty of digital asset management vendors on the market who propose their systems and pics.io is one of the most progressive and modern among them.

Check this 90-second animation to know what is Digital Asset Management (DAM) system and how it works for businesses and organizations.
http://pics.io/digital-asset-management
Digital asset management definition, according to Wikipedia, tells that this notion consists of management tasks and decisions surrounding the ingestion, annotation, cataloguing, storage, retrieval and distribution of digital assets. In simple words digital asset management solutions or systems allow to keep, organize, retrieve and use different media assets. But what is a digital asset? Digital assets are pictures, photos, drawings, video, audio documents, etc. There are plenty of digital asset management vendors on the market who propose their systems and pics.io is one of the most progressive and modern among them.

Triad Systems Control Data 9427H 14" disc drive

A multi-tasking, multi-user business computer from the late 1970's - early 1980's. If you can provide a good home for it, such as at a college or museum, let m...

A multi-tasking, multi-user business computer from the late 1970's - early 1980's. If you can provide a good home for it, such as at a college or museum, let me know and maybe we can work something out. woodywrkng@gmail.com

A multi-tasking, multi-user business computer from the late 1970's - early 1980's. If you can provide a good home for it, such as at a college or museum, let me know and maybe we can work something out. woodywrkng@gmail.com

Authors:
Liang Zhang, LinkedIn CorporationBenjamin Le, LinkedIn Corporation
NadiaFawaz, LinkedIn Corporation
Ganesh Venkataraman, LinkedIn Corporation
Abstract:
Deep learning has been widely successful in solving complex tasks such as image recognition (ImageNet), speech recognition, machine translation, etc. In the area of personalized recommender systems, deep learning has started showing promising advances in recent years. The key to success of deep learning in personalized recommender systems is its ability to learn distributed representations of users’ and items’ attributes in low dimensional dense vector space and combine these to recommend relevant items to users. To address scalability, the implementation of a recommendation system at web scale often leverages components from information retrieval systems, such as inverted indexes where a query is constructed from a user’s attribute and context, learning to rank techniques. Additionally, it relies on machine learning models to predict the relevance of items, such as collaborative filtering. In this tutorial, we present ways to leverage deep learning towards improving recommender system. The tutorial is divided into four parts: (1) In the first part, we will present an overview of concepts in deep learning which are pertinent to recommender systems including sequence modeling, word embedding and named entity recognition. (2) In the second part, we will present how these fundamental building blocks can be used to improve a recommender system at scale. (3) The third part presents a few case studies from large scale recommender systems at LinkedIn and some of the challenges we faced while getting deep learning to work in production.
Link to tutorial: https://engineering.linkedin.com/data/publications/kdd-2017/deep-learning-tutorial
More on http://www.kdd.org/kdd2017/
KDD2017 Conference is published on http://videolectures.net/

Authors:
Liang Zhang, LinkedIn CorporationBenjamin Le, LinkedIn Corporation
NadiaFawaz, LinkedIn Corporation
Ganesh Venkataraman, LinkedIn Corporation
Abstract:
Deep learning has been widely successful in solving complex tasks such as image recognition (ImageNet), speech recognition, machine translation, etc. In the area of personalized recommender systems, deep learning has started showing promising advances in recent years. The key to success of deep learning in personalized recommender systems is its ability to learn distributed representations of users’ and items’ attributes in low dimensional dense vector space and combine these to recommend relevant items to users. To address scalability, the implementation of a recommendation system at web scale often leverages components from information retrieval systems, such as inverted indexes where a query is constructed from a user’s attribute and context, learning to rank techniques. Additionally, it relies on machine learning models to predict the relevance of items, such as collaborative filtering. In this tutorial, we present ways to leverage deep learning towards improving recommender system. The tutorial is divided into four parts: (1) In the first part, we will present an overview of concepts in deep learning which are pertinent to recommender systems including sequence modeling, word embedding and named entity recognition. (2) In the second part, we will present how these fundamental building blocks can be used to improve a recommender system at scale. (3) The third part presents a few case studies from large scale recommender systems at LinkedIn and some of the challenges we faced while getting deep learning to work in production.
Link to tutorial: https://engineering.linkedin.com/data/publications/kdd-2017/deep-learning-tutorial
More on http://www.kdd.org/kdd2017/
KDD2017 Conference is published on http://videolectures.net/

Recommendation Systems - Learn Python for Data Science #3

In this video, we build our own recommendation system that suggests movies a user would like in 40 lines of Python using the LightFM recommendation library. I s...

In this video, we build our own recommendation system that suggests movies a user would like in 40 lines of Python using the LightFM recommendation library. I start off by talking about why we need recommendation systems, then we dive straight into installing our dependencies and writing our script.
The coding challenge for this video is here:
https://github.com/llSourcell/recommender_system_challenge
The winner of last weeks coding challenge (Rohan Verma):
https://twitter-sentiment-csv.herokuapp.com/
https://t.co/4eg8UdlaSB
The runner up (Arnaud Delauney):
https://github.com/arnauddelaunay/twitter_sentiment_challenge
I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/
The LightFM Python Library:
https://github.com/lyst/lightfm/tree/master/lightfm
Some great learning resources on recommender systems:
http://blogs.gartner.com/martin-kihn/how-to-build-a-recommender-system-in-python/
https://www.analyticsvidhya.com/blog/2015/08/beginners-guide-learn-content-based-recommender-systems/
http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/
http://blog.manugarri.com/a-short-introduction-to-recommendation-systems/
Best book to become a Python God:
https://learnpythonthehardway.org/
Please share this video, like, comment and subscribe! That's what keeps me going.
Please support me on Patreon!:
https://www.patreon.com/user?u=3191693
Follow me:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

In this video, we build our own recommendation system that suggests movies a user would like in 40 lines of Python using the LightFM recommendation library. I start off by talking about why we need recommendation systems, then we dive straight into installing our dependencies and writing our script.
The coding challenge for this video is here:
https://github.com/llSourcell/recommender_system_challenge
The winner of last weeks coding challenge (Rohan Verma):
https://twitter-sentiment-csv.herokuapp.com/
https://t.co/4eg8UdlaSB
The runner up (Arnaud Delauney):
https://github.com/arnauddelaunay/twitter_sentiment_challenge
I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/
The LightFM Python Library:
https://github.com/lyst/lightfm/tree/master/lightfm
Some great learning resources on recommender systems:
http://blogs.gartner.com/martin-kihn/how-to-build-a-recommender-system-in-python/
https://www.analyticsvidhya.com/blog/2015/08/beginners-guide-learn-content-based-recommender-systems/
http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/
http://blog.manugarri.com/a-short-introduction-to-recommendation-systems/
Best book to become a Python God:
https://learnpythonthehardway.org/
Please share this video, like, comment and subscribe! That's what keeps me going.
Please support me on Patreon!:
https://www.patreon.com/user?u=3191693
Follow me:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

What is an API?

What exactly is an API? Finally learn for yourself in this helpful video from MuleSoft, the API experts. https://www.mulesoft.com/platform/api
The textbook de...

What exactly is an API? Finally learn for yourself in this helpful video from MuleSoft, the API experts. https://www.mulesoft.com/platform/api
The textbook definition goes something like this:
“An application programming interface (API) is a set of routines, protocols, and tools for building software applications. An API expresses a software component in terms of its operations, inputs, outputs, and underlying types. An API defines functionalities that are independent of their respective implementations, which allows definitions and implementations to vary without compromising each other. A good API makes it easier to develop a program by providing all the building blocks.
APIs often come in the form of a library that includes specifications for routines, data structures, object classes, and variables. In other cases, notably SOAP and REST services, an API is simply a specification of remote calls exposed to the API consumers.
An API specification can take many forms, including an International Standard, such as POSIX, vendor documentation, such as the Microsoft Windows API, or the libraries of a programming language, e.g., the Standard Template Library in C++ or the Java APIs.
An API differs from an application binary interface (ABI) in that an API is source code-based while an ABI is a binary interface. For instance POSIX is an API, while the Linux Standard Base provides an ABI”.
To speak plainly, an API is the messenger that runs and delivers your request to the provider you’re requesting it from, and then delivers the response back to you.
To give you a familiar example, think of an API as a waiter in a restaurant.
Imagine you’re sitting at the table with a menu of choices to order from, and the kitchen is the provider who will fulfill your order.
What’s missing is the critical link to communicate your order to the kitchen and deliver your food back to your table.
That’s where the waiter (or API) comes in. ”AHEM”
The waiter takes your order, delivers it to the kitchen, and then delivers the food (or response) back to you. (Hopefully without letting your order crash if designed correctly)
Now that we’ve whetted your appetite, let’s apply this to a real API example. In keeping with our theme, let’s book a flight to a culinary capital – Paris.
You’re probably familiar with the process of searching for airline flights online. Just like at a restaurant, you have a menu of options to choose from ( a dropdown menu in this case). You choose a departure city and date, a return city and date, cabin class, and other variables (like meal or seating, baggage or pet requests)
In order to book your flight, you interact with the airline’s website to access the airline’s database to see if any seats are available on those dates, and what the cost might be based on certain variables.
But, what if you are not using the airline’s website, which has direct access to the information? What if you are using online travel service that aggregates information from many different airlines? Just like a human interacts with the airline’s website to get that information, an application interacts with the airline’s API.
The API is the interface that, like your helpful waiter, runs and and delivers the data from that online travel service to the airline’s systems over the Internet.
It also then takes the airline’s response to your request and delivers right back to the online travel service .
And through each step of the process it facilitates that interaction between the travel service and the airline’s systems - from seat selection to payment and booking.
So now you can see that it’s APIs that make it possible for us all to use travel sites. They interface with with airlines’ APIs to gather information in order to present options back to us
The same goes for all interactions between applications, data and devices - they all have API’s that allow computers to operate them, and that's what ultimately creates connectivity.
API’s provide a standard way of accessing any application, data or device whether it is shopping from your phone, or accessing cloud applications at work.
So, whenever you think of an API, just think of it as your waiter running back and forth between applications, databases and devices to deliver data and create the connectivity that puts the world at our fingertips. And whenever you think of creating an API, think MuleSoft.

What exactly is an API? Finally learn for yourself in this helpful video from MuleSoft, the API experts. https://www.mulesoft.com/platform/api
The textbook definition goes something like this:
“An application programming interface (API) is a set of routines, protocols, and tools for building software applications. An API expresses a software component in terms of its operations, inputs, outputs, and underlying types. An API defines functionalities that are independent of their respective implementations, which allows definitions and implementations to vary without compromising each other. A good API makes it easier to develop a program by providing all the building blocks.
APIs often come in the form of a library that includes specifications for routines, data structures, object classes, and variables. In other cases, notably SOAP and REST services, an API is simply a specification of remote calls exposed to the API consumers.
An API specification can take many forms, including an International Standard, such as POSIX, vendor documentation, such as the Microsoft Windows API, or the libraries of a programming language, e.g., the Standard Template Library in C++ or the Java APIs.
An API differs from an application binary interface (ABI) in that an API is source code-based while an ABI is a binary interface. For instance POSIX is an API, while the Linux Standard Base provides an ABI”.
To speak plainly, an API is the messenger that runs and delivers your request to the provider you’re requesting it from, and then delivers the response back to you.
To give you a familiar example, think of an API as a waiter in a restaurant.
Imagine you’re sitting at the table with a menu of choices to order from, and the kitchen is the provider who will fulfill your order.
What’s missing is the critical link to communicate your order to the kitchen and deliver your food back to your table.
That’s where the waiter (or API) comes in. ”AHEM”
The waiter takes your order, delivers it to the kitchen, and then delivers the food (or response) back to you. (Hopefully without letting your order crash if designed correctly)
Now that we’ve whetted your appetite, let’s apply this to a real API example. In keeping with our theme, let’s book a flight to a culinary capital – Paris.
You’re probably familiar with the process of searching for airline flights online. Just like at a restaurant, you have a menu of options to choose from ( a dropdown menu in this case). You choose a departure city and date, a return city and date, cabin class, and other variables (like meal or seating, baggage or pet requests)
In order to book your flight, you interact with the airline’s website to access the airline’s database to see if any seats are available on those dates, and what the cost might be based on certain variables.
But, what if you are not using the airline’s website, which has direct access to the information? What if you are using online travel service that aggregates information from many different airlines? Just like a human interacts with the airline’s website to get that information, an application interacts with the airline’s API.
The API is the interface that, like your helpful waiter, runs and and delivers the data from that online travel service to the airline’s systems over the Internet.
It also then takes the airline’s response to your request and delivers right back to the online travel service .
And through each step of the process it facilitates that interaction between the travel service and the airline’s systems - from seat selection to payment and booking.
So now you can see that it’s APIs that make it possible for us all to use travel sites. They interface with with airlines’ APIs to gather information in order to present options back to us
The same goes for all interactions between applications, data and devices - they all have API’s that allow computers to operate them, and that's what ultimately creates connectivity.
API’s provide a standard way of accessing any application, data or device whether it is shopping from your phone, or accessing cloud applications at work.
So, whenever you think of an API, just think of it as your waiter running back and forth between applications, databases and devices to deliver data and create the connectivity that puts the world at our fingertips. And whenever you think of creating an API, think MuleSoft.

Managing big data vision systems

Optimal has developed a bespoke machine vision system for the real-time 100 percent inspection of a thin film product used in the manufacture of electronic comp...

Optimal has developed a bespoke machine vision system for the real-time 100 percent inspection of a thin film product used in the manufacture of electronic components. The new system builds on Optimal's 26 years of systems integration experience and makes use of the company's synTI® integrated Print and Inspect system software.
Optimal’s customer for the new system wanted to replace its previous sample-based quality assurance regime with a more detailed 100 percent inspection approach, but it was concerned that its high manufacturing rates and detailed inspection requirements would make the required level of speed and accuracy difficult to achieve. The material travels at relatively high speed, and the inspection system needs to spot tiny defects in a web 900mm wide, as well as recording very accurate dimensional measurements.
Optimal tackled the problem with a system that uses three, synchronized high resolution, high speed contact image sensors (CIS) installed on the customer’s production line between the manufacture of the film material and downstream slitting and packaging operations. One camera inspects the top of the web of material; the other two are focused on the underside.
The inspection system checks for defects in bands of dark and light coloured coatings on the film, and measures the precise width of the coloured bands. The inspection data is processed by the synTI® software and displayed in real time on the production line, so that operators can check the performance of their upstream processes and make any adjustments or interventions necessary to keep quality within the required tolerance limits.
A summary of the inspection information is also sent automatically to label printing equipment to be added to every batch of material prior to dispatch, and all data is stored in an online database to permit later management review.
With three high resolution sensors each running at up to 10kHz frame rate, the system can generate and manage up to a Gigabit of data every second, although the actual stored data is not that high as the software is able to process the raw data into more a more manageable format.
“Thanks to advances in technology like high speed cameras, high speed communications and powerful processors, our synTI® system can now manage the process in real time.’ says GeoffNorwood, ApplicationsEngineer for vision systems at Optimal. ‘The system means our customer can now inspect 100 percent of their product, 100 percent of the time.”
The synTI® software used to run the film inspection system runs on four powerful PCs which are housed with the rest of the control hardware in a racked cabinet, also built-up and supplied by Optimal.
This combination of highly capable sensors, fast data transfer and powerful processing capabilities is allowing Optimal to solve an increasing number of challenging inspection problems for its customers “Modern cameras can do measurements, or use advanced tools like feature recognition or optical character recognition too,” notes Norwood.
“In this case, we were using a small number of high resolution cameras, but in other examples we might use larger networks of simpler devices. synTI® will interface with a large number of measurement and output devices from cameras to check weigh scales, labellers or laser marking systems.”
While Optimal is often asked to develop systems for continuous manufacturing applications like this example, its skills are also increasingly being used in high speed discrete manufacturing, where they have been applied to a range of tasks, including the detection of marks, stains and defects, non contact measurement and the identification of products by vision, character recognition or code reading tools.
“The ability to process high volumes of data in real time opens up a new world of possibilities in machine vision,” concludes Geoff Norwood. “From 3D data acquisition to the use of image processing on ultra high speed production lines.”
CompanyContact
Optimal Industrial AutomationLimited : Martin Gadsby
Tel: +44 (0) 1454 333222 Fax: +44 (0) 1454 322 240
Web: www.optimal-ltd.co.uk
Email: mgadsby@optimal-ltd.co.uk

Optimal has developed a bespoke machine vision system for the real-time 100 percent inspection of a thin film product used in the manufacture of electronic components. The new system builds on Optimal's 26 years of systems integration experience and makes use of the company's synTI® integrated Print and Inspect system software.
Optimal’s customer for the new system wanted to replace its previous sample-based quality assurance regime with a more detailed 100 percent inspection approach, but it was concerned that its high manufacturing rates and detailed inspection requirements would make the required level of speed and accuracy difficult to achieve. The material travels at relatively high speed, and the inspection system needs to spot tiny defects in a web 900mm wide, as well as recording very accurate dimensional measurements.
Optimal tackled the problem with a system that uses three, synchronized high resolution, high speed contact image sensors (CIS) installed on the customer’s production line between the manufacture of the film material and downstream slitting and packaging operations. One camera inspects the top of the web of material; the other two are focused on the underside.
The inspection system checks for defects in bands of dark and light coloured coatings on the film, and measures the precise width of the coloured bands. The inspection data is processed by the synTI® software and displayed in real time on the production line, so that operators can check the performance of their upstream processes and make any adjustments or interventions necessary to keep quality within the required tolerance limits.
A summary of the inspection information is also sent automatically to label printing equipment to be added to every batch of material prior to dispatch, and all data is stored in an online database to permit later management review.
With three high resolution sensors each running at up to 10kHz frame rate, the system can generate and manage up to a Gigabit of data every second, although the actual stored data is not that high as the software is able to process the raw data into more a more manageable format.
“Thanks to advances in technology like high speed cameras, high speed communications and powerful processors, our synTI® system can now manage the process in real time.’ says GeoffNorwood, ApplicationsEngineer for vision systems at Optimal. ‘The system means our customer can now inspect 100 percent of their product, 100 percent of the time.”
The synTI® software used to run the film inspection system runs on four powerful PCs which are housed with the rest of the control hardware in a racked cabinet, also built-up and supplied by Optimal.
This combination of highly capable sensors, fast data transfer and powerful processing capabilities is allowing Optimal to solve an increasing number of challenging inspection problems for its customers “Modern cameras can do measurements, or use advanced tools like feature recognition or optical character recognition too,” notes Norwood.
“In this case, we were using a small number of high resolution cameras, but in other examples we might use larger networks of simpler devices. synTI® will interface with a large number of measurement and output devices from cameras to check weigh scales, labellers or laser marking systems.”
While Optimal is often asked to develop systems for continuous manufacturing applications like this example, its skills are also increasingly being used in high speed discrete manufacturing, where they have been applied to a range of tasks, including the detection of marks, stains and defects, non contact measurement and the identification of products by vision, character recognition or code reading tools.
“The ability to process high volumes of data in real time opens up a new world of possibilities in machine vision,” concludes Geoff Norwood. “From 3D data acquisition to the use of image processing on ultra high speed production lines.”
CompanyContact
Optimal Industrial AutomationLimited : Martin Gadsby
Tel: +44 (0) 1454 333222 Fax: +44 (0) 1454 322 240
Web: www.optimal-ltd.co.uk
Email: mgadsby@optimal-ltd.co.uk

How to combine data from your website, CRM, ERP and other systems

Get the webinar replay and slide deck here: https://www.owox.com/c/h3
When you have your data collected in a number of systems — ERP, CRM, advertising services...

Get the webinar replay and slide deck here: https://www.owox.com/c/h3
When you have your data collected in a number of systems — ERP, CRM, advertising services, price intelligence services, etc. — piecing it together manually is often a tedious and error-prone task.
We will look at specific examples of system integrations, and give you examples of reports and charts which you can create by combining data.
Join the webinar and find out:
1. What are the difficulties in combining data from multiple services.
2. How to combine data into a single system using DataVirtuality and OWOX BI https://www.owox.com/c/fg
3. Examples of imports and exports to and from Google BigQuery.
4. Examples of informative reports and charts based on complete data from multiple sources.
The webinar will be useful to:
Data analysts and technical experts who are looking to save time by automating manual processes.
More webinars about Google services best practices for Ecommerce businesses: https://www.owox.com/c/g2

Get the webinar replay and slide deck here: https://www.owox.com/c/h3
When you have your data collected in a number of systems — ERP, CRM, advertising services, price intelligence services, etc. — piecing it together manually is often a tedious and error-prone task.
We will look at specific examples of system integrations, and give you examples of reports and charts which you can create by combining data.
Join the webinar and find out:
1. What are the difficulties in combining data from multiple services.
2. How to combine data into a single system using DataVirtuality and OWOX BI https://www.owox.com/c/fg
3. Examples of imports and exports to and from Google BigQuery.
4. Examples of informative reports and charts based on complete data from multiple sources.
The webinar will be useful to:
Data analysts and technical experts who are looking to save time by automating manual processes.
More webinars about Google services best practices for Ecommerce businesses: https://www.owox.com/c/g2

The CaptiView image injection module displays data from Image Guided Surgery (IGS) systems directly in the eyepieces of a Leica microscope during neurosurgery. Surgeons therefore have the high-contrast, high-resolution data they need to make confident surgical decisions - without taking their eyes off the patient.
In this video you can see data from the neuronavigation software Cranial 3.1 from BrainLab overlaid onto the live surgical image during neurosurgery. The video is courtesy of Mount Sinai Health System, New York.
Get full details about CaptiView image injection:
http://www.leica-microsystems.com/products/surgical-microscopes/neurosurgery-spine/details/product/captiview/
Read more about surgical image injection and IGS in our ScienceLab portal:
http://www.leica-microsystems.com/science-lab/topics/surgical-microscopy/

The CaptiView image injection module displays data from Image Guided Surgery (IGS) systems directly in the eyepieces of a Leica microscope during neurosurgery. Surgeons therefore have the high-contrast, high-resolution data they need to make confident surgical decisions - without taking their eyes off the patient.
In this video you can see data from the neuronavigation software Cranial 3.1 from BrainLab overlaid onto the live surgical image during neurosurgery. The video is courtesy of Mount Sinai Health System, New York.
Get full details about CaptiView image injection:
http://www.leica-microsystems.com/products/surgical-microscopes/neurosurgery-spine/details/product/captiview/
Read more about surgical image injection and IGS in our ScienceLab portal:
http://www.leica-microsystems.com/science-lab/topics/surgical-microscopy/

Deep Learning for Personalized Search and Recommender Systems part 1

Authors:
Liang Zhang, LinkedIn CorporationBenjamin Le, LinkedIn Corporation
NadiaFawaz, LinkedIn Corporation
Ganesh Venkataraman, LinkedIn Corporation
Abstract:
Deep learning has been widely successful in solving complex tasks such as image recognition (ImageNet), speech recognition, machine translation, etc. In the area of personalized recommender systems, deep learning has started showing promising advances in recent years. The key to success of deep learning in personalized recommender systems is its ability to learn distributed representations of users’ and items’ attributes in low dimensional dense vector space and combine these to recommend relevant items to users. To address scalability, the implementation of a recommendation system at web scale often leverages components fro...

published: 17 Nov 2017

Deep Learning: Intelligence from Big Data

Deep Learning: Intelligence from Big Data
Tue Sep 16, 2014 6:00 pm - 8:30 pm
Stanford Graduate School of BusinessKnightManagementCenter – CemexAuditorium
641 Knight Way, Stanford, CA
A machine learning approach inspired by the human brain, Deep Learning is taking many industries by storm. Empowered by the latest generation of commodity computing, Deep Learning begins to derive significant value from Big Data. It has already radically improved the computer’s ability to recognize speech and identify objects in images, two fundamental hallmarks of human intelligence.
Industry giants such as Google, Facebook, and Baidu have acquired most of the dominant players in this space to improve their product offerings. At the same time, startup entrepreneurs are creating a new paradigm, Intellige...

published: 31 Oct 2014

SAXually Explicit Images: Data Mining Large Shape Databases

Google TechTalks
May 12, 2006
Eamonn Keogh
ABSTRACT
The problem of indexing large collections of time series and images has received much attention in the last decade, however we argue that there is potentially great untapped utility in data mining such collections. Consider the following two concrete examples of problems in data mining.
MotifDiscovery (duplication detection): Given a large repository of time series or images, find approximately repeated patterns/images.
Discord Discovery: Given a large repository of time series or images, find the most unusual time series/image.
As we will show, both these problems have applications in fields as diverse as anthropology, crime...

How to create ,Save,Update ,Delete and SearchStudentProfile information using Visual basic and Ms Access-Step By StepVB6Control used are Textbox, OptionBox,Combobox,Picturebox,DatePicker ,CommonDialog controls
Features of Application are:
1.How to design the VB form and add various controls i.e Textbox, OptionBox,Combobox,Picturebox,DatePicker ,Common Dialog controls onto the form.
2.How to create database object at run time and do the database connectivity.
3.How to load the image onto the form using commondialog control and also Save /Retrieve the Image or Picture from the database.
4.How to save the values selected from Optionbox and Combobox into the database and retrieve them when required.
5.How to Use datepicker control and Save the Date into the Database.
6.How to Save ,De...

Agriculture: The Next Machine-Learning Frontier | Data Dialogs 2016

In the past decade the high-tech industry has been revolutionized by machine learning algorithms applied to everything from self-driving cars to personalized recommendation systems in domains such as healthcare and marketing.
Agriculture is a less familiar research domain among the machine learning community. Nevertheless, this domain offers unique and challenging scientific opportunities related to the spatio-temporal nature of the data, the multi-resolution data sources, the interaction with environmental models.
In this talk, I will introduce The Climate Corporation and how its using Data Science to tackle some of the most challenging problems growers face these days. Furthermore, I will present a few of our ongoing research projects in the fields of agronomy, remote sensing and weath...

published: 08 Dec 2016

The Complete MATLAB Course: Beginner to Advanced!

Get The CompleteMATLABCourse Bundle for 1 on 1 help!
https://josephdelgadillo.com/product/matlab-course-bundle/
Enroll in the FREE Teachable course!
http://jtdigital.teachable.com/p/matlab
Time Stamps
00:51 What isMatlab, how to download Matlab, and where to find help
07:52 Introduction to the Matlab basic syntax, command window, and working directory
18:35 Basic matrix arithmetic in Matlab including an overview of different operators
27:30 Learn the built in functions and constants and how to write your own functions
42:20 Solving linear equations using Matlab
53:33 For loops, while loops, and if statements
1:09:15 Exploring different types of data
1:20:27 Plotting data using the Fibonacci Sequence
1:30:45 Plots useful for data analysis
1:38:49 How to load and save data
1:46:46 Subpl...

Authors:
Liang Zhang, LinkedIn CorporationBenjamin Le, LinkedIn Corporation
NadiaFawaz, LinkedIn Corporation
Ganesh Venkataraman, LinkedIn Corporation
Abstract:
Deep learning has been widely successful in solving complex tasks such as image recognition (ImageNet), speech recognition, machine translation, etc. In the area of personalized recommender systems, deep learning has started showing promising advances in recent years. The key to success of deep learning in personalized recommender systems is its ability to learn distributed representations of users’ and items’ attributes in low dimensional dense vector space and combine these to recommend relevant items to users. To address scalability, the implementation of a recommendation system at web scale often leverages components from information retrieval systems, such as inverted indexes where a query is constructed from a user’s attribute and context, learning to rank techniques. Additionally, it relies on machine learning models to predict the relevance of items, such as collaborative filtering. In this tutorial, we present ways to leverage deep learning towards improving recommender system. The tutorial is divided into four parts: (1) In the first part, we will present an overview of concepts in deep learning which are pertinent to recommender systems including sequence modeling, word embedding and named entity recognition. (2) In the second part, we will present how these fundamental building blocks can be used to improve a recommender system at scale. (3) The third part presents a few case studies from large scale recommender systems at LinkedIn and some of the challenges we faced while getting deep learning to work in production.
Link to tutorial: https://engineering.linkedin.com/data/publications/kdd-2017/deep-learning-tutorial
More on http://www.kdd.org/kdd2017/
KDD2017 Conference is published on http://videolectures.net/

Authors:
Liang Zhang, LinkedIn CorporationBenjamin Le, LinkedIn Corporation
NadiaFawaz, LinkedIn Corporation
Ganesh Venkataraman, LinkedIn Corporation
Abstract:
Deep learning has been widely successful in solving complex tasks such as image recognition (ImageNet), speech recognition, machine translation, etc. In the area of personalized recommender systems, deep learning has started showing promising advances in recent years. The key to success of deep learning in personalized recommender systems is its ability to learn distributed representations of users’ and items’ attributes in low dimensional dense vector space and combine these to recommend relevant items to users. To address scalability, the implementation of a recommendation system at web scale often leverages components from information retrieval systems, such as inverted indexes where a query is constructed from a user’s attribute and context, learning to rank techniques. Additionally, it relies on machine learning models to predict the relevance of items, such as collaborative filtering. In this tutorial, we present ways to leverage deep learning towards improving recommender system. The tutorial is divided into four parts: (1) In the first part, we will present an overview of concepts in deep learning which are pertinent to recommender systems including sequence modeling, word embedding and named entity recognition. (2) In the second part, we will present how these fundamental building blocks can be used to improve a recommender system at scale. (3) The third part presents a few case studies from large scale recommender systems at LinkedIn and some of the challenges we faced while getting deep learning to work in production.
Link to tutorial: https://engineering.linkedin.com/data/publications/kdd-2017/deep-learning-tutorial
More on http://www.kdd.org/kdd2017/
KDD2017 Conference is published on http://videolectures.net/

SAXually Explicit Images: Data Mining Large Shape Databases

Google TechTalks
May 12, 2006
Eamonn Keogh
ABSTRACT
The problem of indexing large collections of time series and images has received much attention in the las...

Google TechTalks
May 12, 2006
Eamonn Keogh
ABSTRACT
The problem of indexing large collections of time series and images has received much attention in the last decade, however we argue that there is potentially great untapped utility in data mining such collections. Consider the following two concrete examples of problems in data mining.
MotifDiscovery (duplication detection): Given a large repository of time series or images, find approximately repeated patterns/images.
Discord Discovery: Given a large repository of time series or images, find the most unusual time series/image.
As we will show, both these problems have applications in fields as diverse as anthropology, crime...

Google TechTalks
May 12, 2006
Eamonn Keogh
ABSTRACT
The problem of indexing large collections of time series and images has received much attention in the last decade, however we argue that there is potentially great untapped utility in data mining such collections. Consider the following two concrete examples of problems in data mining.
MotifDiscovery (duplication detection): Given a large repository of time series or images, find approximately repeated patterns/images.
Discord Discovery: Given a large repository of time series or images, find the most unusual time series/image.
As we will show, both these problems have applications in fields as diverse as anthropology, crime...

Agriculture: The Next Machine-Learning Frontier | Data Dialogs 2016

In the past decade the high-tech industry has been revolutionized by machine learning algorithms applied to everything from self-driving cars to personalized re...

In the past decade the high-tech industry has been revolutionized by machine learning algorithms applied to everything from self-driving cars to personalized recommendation systems in domains such as healthcare and marketing.
Agriculture is a less familiar research domain among the machine learning community. Nevertheless, this domain offers unique and challenging scientific opportunities related to the spatio-temporal nature of the data, the multi-resolution data sources, the interaction with environmental models.
In this talk, I will introduce The Climate Corporation and how its using Data Science to tackle some of the most challenging problems growers face these days. Furthermore, I will present a few of our ongoing research projects in the fields of agronomy, remote sensing and weather modeling and our philosophy of solving these problems.
https://datadialogs.ischool.berkeley.edu/2016/schedule/agriculture-next-machine-learning-frontier
. . . . . . . . . . . . . . . . . . . . . . . . . . . . .Sivan Noiman
Director of Data Science
Data Science Center for Excellence for The Climate Corporation
Sivan is a Director of Data Science for the DataScienceCenter of Excellence for The Climate Corporation. In this capacity, Sivan and her team are supporting the development of innovative data-driven solutions to help growers optimize their operations across the globe. In addition, as part of her role Sivan is helping to develop and adopt best-practices for leading Data Science teams.
Sivan began her career in the Israeli military serving as an instructor for an anti-tank missile unit. She then transitioned to school and received her undergraduate degree in Industrial Engineering and a Master in Statistics from the Technion, Israel Institute of Technology. She later moved to the U.S. to complete a Ph.D. degree in Statistics from The Wharton School, University of Pennsylvania.
Sivan’s experiences from the military, academia and private industry shaped her leadership style. She is an enthusiastic disagreeable giver and a constant empirical driven learner. Sivan is also a proud mother of two adorable boys.

In the past decade the high-tech industry has been revolutionized by machine learning algorithms applied to everything from self-driving cars to personalized recommendation systems in domains such as healthcare and marketing.
Agriculture is a less familiar research domain among the machine learning community. Nevertheless, this domain offers unique and challenging scientific opportunities related to the spatio-temporal nature of the data, the multi-resolution data sources, the interaction with environmental models.
In this talk, I will introduce The Climate Corporation and how its using Data Science to tackle some of the most challenging problems growers face these days. Furthermore, I will present a few of our ongoing research projects in the fields of agronomy, remote sensing and weather modeling and our philosophy of solving these problems.
https://datadialogs.ischool.berkeley.edu/2016/schedule/agriculture-next-machine-learning-frontier
. . . . . . . . . . . . . . . . . . . . . . . . . . . . .Sivan Noiman
Director of Data Science
Data Science Center for Excellence for The Climate Corporation
Sivan is a Director of Data Science for the DataScienceCenter of Excellence for The Climate Corporation. In this capacity, Sivan and her team are supporting the development of innovative data-driven solutions to help growers optimize their operations across the globe. In addition, as part of her role Sivan is helping to develop and adopt best-practices for leading Data Science teams.
Sivan began her career in the Israeli military serving as an instructor for an anti-tank missile unit. She then transitioned to school and received her undergraduate degree in Industrial Engineering and a Master in Statistics from the Technion, Israel Institute of Technology. She later moved to the U.S. to complete a Ph.D. degree in Statistics from The Wharton School, University of Pennsylvania.
Sivan’s experiences from the military, academia and private industry shaped her leadership style. She is an enthusiastic disagreeable giver and a constant empirical driven learner. Sivan is also a proud mother of two adorable boys.

Get The CompleteMATLABCourse Bundle for 1 on 1 help!
https://josephdelgadillo.com/product/matlab-course-bundle/
Enroll in the FREE Teachable course!
http://jtdigital.teachable.com/p/matlab
Time Stamps
00:51 What isMatlab, how to download Matlab, and where to find help
07:52 Introduction to the Matlab basic syntax, command window, and working directory
18:35 Basic matrix arithmetic in Matlab including an overview of different operators
27:30 Learn the built in functions and constants and how to write your own functions
42:20 Solving linear equations using Matlab
53:33 For loops, while loops, and if statements
1:09:15 Exploring different types of data
1:20:27 Plotting data using the Fibonacci Sequence
1:30:45 Plots useful for data analysis
1:38:49 How to load and save data
1:46:46 Subplots, 3D plots, and labeling plots
1:55:35 Sound is a wave of air particles
2:05:33 Reversing a signal
2:12:57 The Fourier transform lets you view the frequency components of a signal
2:27:25 Fourier transform of a sine wave
2:35:14 Applying a low-pass filter to an audio stream
2:43:50 To store images in a computer you must sample the resolution
2:50:13 Basic image manipulation including how to flip images
2:57:29 Convolution allows you to blur an image
3:02:51 A Gaussian filter allows you reduce image noise and detail
3:08:55 Blur and edge detection using the Gaussian filter
3:16:39 Introduction to Matlab & probability
3:19:47 Measuring probability
3:26:53 Generating random values
3:35:40 Birthday paradox
3:43:25 Continuous variables
3:48:00 Mean and variance
3:55:24 Gaussian (normal) distribution
4:03:21 Test for normality
4:10:32 2 sample tests
4:16:28 Multivariate Gaussian

Get The CompleteMATLABCourse Bundle for 1 on 1 help!
https://josephdelgadillo.com/product/matlab-course-bundle/
Enroll in the FREE Teachable course!
http://jtdigital.teachable.com/p/matlab
Time Stamps
00:51 What isMatlab, how to download Matlab, and where to find help
07:52 Introduction to the Matlab basic syntax, command window, and working directory
18:35 Basic matrix arithmetic in Matlab including an overview of different operators
27:30 Learn the built in functions and constants and how to write your own functions
42:20 Solving linear equations using Matlab
53:33 For loops, while loops, and if statements
1:09:15 Exploring different types of data
1:20:27 Plotting data using the Fibonacci Sequence
1:30:45 Plots useful for data analysis
1:38:49 How to load and save data
1:46:46 Subplots, 3D plots, and labeling plots
1:55:35 Sound is a wave of air particles
2:05:33 Reversing a signal
2:12:57 The Fourier transform lets you view the frequency components of a signal
2:27:25 Fourier transform of a sine wave
2:35:14 Applying a low-pass filter to an audio stream
2:43:50 To store images in a computer you must sample the resolution
2:50:13 Basic image manipulation including how to flip images
2:57:29 Convolution allows you to blur an image
3:02:51 A Gaussian filter allows you reduce image noise and detail
3:08:55 Blur and edge detection using the Gaussian filter
3:16:39 Introduction to Matlab & probability
3:19:47 Measuring probability
3:26:53 Generating random values
3:35:40 Birthday paradox
3:43:25 Continuous variables
3:48:00 Mean and variance
3:55:24 Gaussian (normal) distribution
4:03:21 Test for normality
4:10:32 2 sample tests
4:16:28 Multivariate Gaussian

Digital Asset Management Explained (Animation)

Check this 90-second animation to know what is Digital Asset Management (DAM) system and how it works for businesses and organizations.
http://pics.io/digital-asset-management
Digital asset management definition, according to Wikipedia, tells that this notion consists of management tasks and decisions surrounding the ingestion, annotation, cataloguing, storage, retrieval and distribution of digital assets. In simple words digital asset management solutions or systems allow to keep, organize, retrieve and use different media assets. But what is a digital asset? Digital assets are pictures, photos, drawings, video, audio documents, etc. There are plenty of digital asset management vendors on the market who propose their systems and pics.io is one of the most progressive and modern among them.

59:54

Lecture - 10 Data Acquisition Systems

Lecture Series on Industrial Automation and Control by Prof. S. Mukhopadhyay, Department o...

Triad Systems Control Data 9427H 14" disc drive

A multi-tasking, multi-user business computer from the late 1970's - early 1980's. If you can provide a good home for it, such as at a college or museum, let me know and maybe we can work something out. woodywrkng@gmail.com

Deep Learning for Personalized Search and Recommender Systems part 1

Authors:
Liang Zhang, LinkedIn CorporationBenjamin Le, LinkedIn Corporation
NadiaFawaz, LinkedIn Corporation
Ganesh Venkataraman, LinkedIn Corporation
Abstract:
Deep learning has been widely successful in solving complex tasks such as image recognition (ImageNet), speech recognition, machine translation, etc. In the area of personalized recommender systems, deep learning has started showing promising advances in recent years. The key to success of deep learning in personalized recommender systems is its ability to learn distributed representations of users’ and items’ attributes in low dimensional dense vector space and combine these to recommend relevant items to users. To address scalability, the implementation of a recommendation system at web scale often leverages components from information retrieval systems, such as inverted indexes where a query is constructed from a user’s attribute and context, learning to rank techniques. Additionally, it relies on machine learning models to predict the relevance of items, such as collaborative filtering. In this tutorial, we present ways to leverage deep learning towards improving recommender system. The tutorial is divided into four parts: (1) In the first part, we will present an overview of concepts in deep learning which are pertinent to recommender systems including sequence modeling, word embedding and named entity recognition. (2) In the second part, we will present how these fundamental building blocks can be used to improve a recommender system at scale. (3) The third part presents a few case studies from large scale recommender systems at LinkedIn and some of the challenges we faced while getting deep learning to work in production.
Link to tutorial: https://engineering.linkedin.com/data/publications/kdd-2017/deep-learning-tutorial
More on http://www.kdd.org/kdd2017/
KDD2017 Conference is published on http://videolectures.net/

6:57

Recommendation Systems - Learn Python for Data Science #3

In this video, we build our own recommendation system that suggests movies a user would li...

Recommendation Systems - Learn Python for Data Science #3

In this video, we build our own recommendation system that suggests movies a user would like in 40 lines of Python using the LightFM recommendation library. I start off by talking about why we need recommendation systems, then we dive straight into installing our dependencies and writing our script.
The coding challenge for this video is here:
https://github.com/llSourcell/recommender_system_challenge
The winner of last weeks coding challenge (Rohan Verma):
https://twitter-sentiment-csv.herokuapp.com/
https://t.co/4eg8UdlaSB
The runner up (Arnaud Delauney):
https://github.com/arnauddelaunay/twitter_sentiment_challenge
I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/
The LightFM Python Library:
https://github.com/lyst/lightfm/tree/master/lightfm
Some great learning resources on recommender systems:
http://blogs.gartner.com/martin-kihn/how-to-build-a-recommender-system-in-python/
https://www.analyticsvidhya.com/blog/2015/08/beginners-guide-learn-content-based-recommender-systems/
http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/
http://blog.manugarri.com/a-short-introduction-to-recommendation-systems/
Best book to become a Python God:
https://learnpythonthehardway.org/
Please share this video, like, comment and subscribe! That's what keeps me going.
Please support me on Patreon!:
https://www.patreon.com/user?u=3191693
Follow me:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

3:25

What is an API?

What exactly is an API? Finally learn for yourself in this helpful video from MuleSoft, th...

What is an API?

What exactly is an API? Finally learn for yourself in this helpful video from MuleSoft, the API experts. https://www.mulesoft.com/platform/api
The textbook definition goes something like this:
“An application programming interface (API) is a set of routines, protocols, and tools for building software applications. An API expresses a software component in terms of its operations, inputs, outputs, and underlying types. An API defines functionalities that are independent of their respective implementations, which allows definitions and implementations to vary without compromising each other. A good API makes it easier to develop a program by providing all the building blocks.
APIs often come in the form of a library that includes specifications for routines, data structures, object classes, and variables. In other cases, notably SOAP and REST services, an API is simply a specification of remote calls exposed to the API consumers.
An API specification can take many forms, including an International Standard, such as POSIX, vendor documentation, such as the Microsoft Windows API, or the libraries of a programming language, e.g., the Standard Template Library in C++ or the Java APIs.
An API differs from an application binary interface (ABI) in that an API is source code-based while an ABI is a binary interface. For instance POSIX is an API, while the Linux Standard Base provides an ABI”.
To speak plainly, an API is the messenger that runs and delivers your request to the provider you’re requesting it from, and then delivers the response back to you.
To give you a familiar example, think of an API as a waiter in a restaurant.
Imagine you’re sitting at the table with a menu of choices to order from, and the kitchen is the provider who will fulfill your order.
What’s missing is the critical link to communicate your order to the kitchen and deliver your food back to your table.
That’s where the waiter (or API) comes in. ”AHEM”
The waiter takes your order, delivers it to the kitchen, and then delivers the food (or response) back to you. (Hopefully without letting your order crash if designed correctly)
Now that we’ve whetted your appetite, let’s apply this to a real API example. In keeping with our theme, let’s book a flight to a culinary capital – Paris.
You’re probably familiar with the process of searching for airline flights online. Just like at a restaurant, you have a menu of options to choose from ( a dropdown menu in this case). You choose a departure city and date, a return city and date, cabin class, and other variables (like meal or seating, baggage or pet requests)
In order to book your flight, you interact with the airline’s website to access the airline’s database to see if any seats are available on those dates, and what the cost might be based on certain variables.
But, what if you are not using the airline’s website, which has direct access to the information? What if you are using online travel service that aggregates information from many different airlines? Just like a human interacts with the airline’s website to get that information, an application interacts with the airline’s API.
The API is the interface that, like your helpful waiter, runs and and delivers the data from that online travel service to the airline’s systems over the Internet.
It also then takes the airline’s response to your request and delivers right back to the online travel service .
And through each step of the process it facilitates that interaction between the travel service and the airline’s systems - from seat selection to payment and booking.
So now you can see that it’s APIs that make it possible for us all to use travel sites. They interface with with airlines’ APIs to gather information in order to present options back to us
The same goes for all interactions between applications, data and devices - they all have API’s that allow computers to operate them, and that's what ultimately creates connectivity.
API’s provide a standard way of accessing any application, data or device whether it is shopping from your phone, or accessing cloud applications at work.
So, whenever you think of an API, just think of it as your waiter running back and forth between applications, databases and devices to deliver data and create the connectivity that puts the world at our fingertips. And whenever you think of creating an API, think MuleSoft.

4:14

Managing big data vision systems

Optimal has developed a bespoke machine vision system for the real-time 100 percent inspec...

Managing big data vision systems

Optimal has developed a bespoke machine vision system for the real-time 100 percent inspection of a thin film product used in the manufacture of electronic components. The new system builds on Optimal's 26 years of systems integration experience and makes use of the company's synTI® integrated Print and Inspect system software.
Optimal’s customer for the new system wanted to replace its previous sample-based quality assurance regime with a more detailed 100 percent inspection approach, but it was concerned that its high manufacturing rates and detailed inspection requirements would make the required level of speed and accuracy difficult to achieve. The material travels at relatively high speed, and the inspection system needs to spot tiny defects in a web 900mm wide, as well as recording very accurate dimensional measurements.
Optimal tackled the problem with a system that uses three, synchronized high resolution, high speed contact image sensors (CIS) installed on the customer’s production line between the manufacture of the film material and downstream slitting and packaging operations. One camera inspects the top of the web of material; the other two are focused on the underside.
The inspection system checks for defects in bands of dark and light coloured coatings on the film, and measures the precise width of the coloured bands. The inspection data is processed by the synTI® software and displayed in real time on the production line, so that operators can check the performance of their upstream processes and make any adjustments or interventions necessary to keep quality within the required tolerance limits.
A summary of the inspection information is also sent automatically to label printing equipment to be added to every batch of material prior to dispatch, and all data is stored in an online database to permit later management review.
With three high resolution sensors each running at up to 10kHz frame rate, the system can generate and manage up to a Gigabit of data every second, although the actual stored data is not that high as the software is able to process the raw data into more a more manageable format.
“Thanks to advances in technology like high speed cameras, high speed communications and powerful processors, our synTI® system can now manage the process in real time.’ says GeoffNorwood, ApplicationsEngineer for vision systems at Optimal. ‘The system means our customer can now inspect 100 percent of their product, 100 percent of the time.”
The synTI® software used to run the film inspection system runs on four powerful PCs which are housed with the rest of the control hardware in a racked cabinet, also built-up and supplied by Optimal.
This combination of highly capable sensors, fast data transfer and powerful processing capabilities is allowing Optimal to solve an increasing number of challenging inspection problems for its customers “Modern cameras can do measurements, or use advanced tools like feature recognition or optical character recognition too,” notes Norwood.
“In this case, we were using a small number of high resolution cameras, but in other examples we might use larger networks of simpler devices. synTI® will interface with a large number of measurement and output devices from cameras to check weigh scales, labellers or laser marking systems.”
While Optimal is often asked to develop systems for continuous manufacturing applications like this example, its skills are also increasingly being used in high speed discrete manufacturing, where they have been applied to a range of tasks, including the detection of marks, stains and defects, non contact measurement and the identification of products by vision, character recognition or code reading tools.
“The ability to process high volumes of data in real time opens up a new world of possibilities in machine vision,” concludes Geoff Norwood. “From 3D data acquisition to the use of image processing on ultra high speed production lines.”
CompanyContact
Optimal Industrial AutomationLimited : Martin Gadsby
Tel: +44 (0) 1454 333222 Fax: +44 (0) 1454 322 240
Web: www.optimal-ltd.co.uk
Email: mgadsby@optimal-ltd.co.uk

How to combine data from your website, CRM, ERP and other systems

Get the webinar replay and slide deck here: https://www.owox.com/c/h3
When you have your data collected in a number of systems — ERP, CRM, advertising services, price intelligence services, etc. — piecing it together manually is often a tedious and error-prone task.
We will look at specific examples of system integrations, and give you examples of reports and charts which you can create by combining data.
Join the webinar and find out:
1. What are the difficulties in combining data from multiple services.
2. How to combine data into a single system using DataVirtuality and OWOX BI https://www.owox.com/c/fg
3. Examples of imports and exports to and from Google BigQuery.
4. Examples of informative reports and charts based on complete data from multiple sources.
The webinar will be useful to:
Data analysts and technical experts who are looking to save time by automating manual processes.
More webinars about Google services best practices for Ecommerce businesses: https://www.owox.com/c/g2

The CaptiView image injection module displays data from Image Guided Surgery (IGS) systems directly in the eyepieces of a Leica microscope during neurosurgery. Surgeons therefore have the high-contrast, high-resolution data they need to make confident surgical decisions - without taking their eyes off the patient.
In this video you can see data from the neuronavigation software Cranial 3.1 from BrainLab overlaid onto the live surgical image during neurosurgery. The video is courtesy of Mount Sinai Health System, New York.
Get full details about CaptiView image injection:
http://www.leica-microsystems.com/products/surgical-microscopes/neurosurgery-spine/details/product/captiview/
Read more about surgical image injection and IGS in our ScienceLab portal:
http://www.leica-microsystems.com/science-lab/topics/surgical-microscopy/

Deep Learning for Personalized Search and Recommender Systems part 1

Authors:
Liang Zhang, LinkedIn CorporationBenjamin Le, LinkedIn Corporation
NadiaFawaz, LinkedIn Corporation
Ganesh Venkataraman, LinkedIn Corporation
Abstract:
Deep learning has been widely successful in solving complex tasks such as image recognition (ImageNet), speech recognition, machine translation, etc. In the area of personalized recommender systems, deep learning has started showing promising advances in recent years. The key to success of deep learning in personalized recommender systems is its ability to learn distributed representations of users’ and items’ attributes in low dimensional dense vector space and combine these to recommend relevant items to users. To address scalability, the implementation of a recommendation system at web scale often leverages components from information retrieval systems, such as inverted indexes where a query is constructed from a user’s attribute and context, learning to rank techniques. Additionally, it relies on machine learning models to predict the relevance of items, such as collaborative filtering. In this tutorial, we present ways to leverage deep learning towards improving recommender system. The tutorial is divided into four parts: (1) In the first part, we will present an overview of concepts in deep learning which are pertinent to recommender systems including sequence modeling, word embedding and named entity recognition. (2) In the second part, we will present how these fundamental building blocks can be used to improve a recommender system at scale. (3) The third part presents a few case studies from large scale recommender systems at LinkedIn and some of the challenges we faced while getting deep learning to work in production.
Link to tutorial: https://engineering.linkedin.com/data/publications/kdd-2017/deep-learning-tutorial
More on http://www.kdd.org/kdd2017/
KDD2017 Conference is published on http://videolectures.net/

SAXually Explicit Images: Data Mining Large Shape Databases

Google TechTalks
May 12, 2006
Eamonn Keogh
ABSTRACT
The problem of indexing large collections of time series and images has received much attention in the last decade, however we argue that there is potentially great untapped utility in data mining such collections. Consider the following two concrete examples of problems in data mining.
MotifDiscovery (duplication detection): Given a large repository of time series or images, find approximately repeated patterns/images.
Discord Discovery: Given a large repository of time series or images, find the most unusual time series/image.
As we will show, both these problems have applications in fields as diverse as anthropology, crime...

Agriculture: The Next Machine-Learning Frontier | Data Dialogs 2016

In the past decade the high-tech industry has been revolutionized by machine learning algorithms applied to everything from self-driving cars to personalized recommendation systems in domains such as healthcare and marketing.
Agriculture is a less familiar research domain among the machine learning community. Nevertheless, this domain offers unique and challenging scientific opportunities related to the spatio-temporal nature of the data, the multi-resolution data sources, the interaction with environmental models.
In this talk, I will introduce The Climate Corporation and how its using Data Science to tackle some of the most challenging problems growers face these days. Furthermore, I will present a few of our ongoing research projects in the fields of agronomy, remote sensing and weather modeling and our philosophy of solving these problems.
https://datadialogs.ischool.berkeley.edu/2016/schedule/agriculture-next-machine-learning-frontier
. . . . . . . . . . . . . . . . . . . . . . . . . . . . .Sivan Noiman
Director of Data Science
Data Science Center for Excellence for The Climate Corporation
Sivan is a Director of Data Science for the DataScienceCenter of Excellence for The Climate Corporation. In this capacity, Sivan and her team are supporting the development of innovative data-driven solutions to help growers optimize their operations across the globe. In addition, as part of her role Sivan is helping to develop and adopt best-practices for leading Data Science teams.
Sivan began her career in the Israeli military serving as an instructor for an anti-tank missile unit. She then transitioned to school and received her undergraduate degree in Industrial Engineering and a Master in Statistics from the Technion, Israel Institute of Technology. She later moved to the U.S. to complete a Ph.D. degree in Statistics from The Wharton School, University of Pennsylvania.
Sivan’s experiences from the military, academia and private industry shaped her leadership style. She is an enthusiastic disagreeable giver and a constant empirical driven learner. Sivan is also a proud mother of two adorable boys.

4:22:09

The Complete MATLAB Course: Beginner to Advanced!

Get The Complete MATLAB Course Bundle for 1 on 1 help!
https://josephdelgadillo.com/produc...

The Complete MATLAB Course: Beginner to Advanced!

Get The CompleteMATLABCourse Bundle for 1 on 1 help!
https://josephdelgadillo.com/product/matlab-course-bundle/
Enroll in the FREE Teachable course!
http://jtdigital.teachable.com/p/matlab
Time Stamps
00:51 What isMatlab, how to download Matlab, and where to find help
07:52 Introduction to the Matlab basic syntax, command window, and working directory
18:35 Basic matrix arithmetic in Matlab including an overview of different operators
27:30 Learn the built in functions and constants and how to write your own functions
42:20 Solving linear equations using Matlab
53:33 For loops, while loops, and if statements
1:09:15 Exploring different types of data
1:20:27 Plotting data using the Fibonacci Sequence
1:30:45 Plots useful for data analysis
1:38:49 How to load and save data
1:46:46 Subplots, 3D plots, and labeling plots
1:55:35 Sound is a wave of air particles
2:05:33 Reversing a signal
2:12:57 The Fourier transform lets you view the frequency components of a signal
2:27:25 Fourier transform of a sine wave
2:35:14 Applying a low-pass filter to an audio stream
2:43:50 To store images in a computer you must sample the resolution
2:50:13 Basic image manipulation including how to flip images
2:57:29 Convolution allows you to blur an image
3:02:51 A Gaussian filter allows you reduce image noise and detail
3:08:55 Blur and edge detection using the Gaussian filter
3:16:39 Introduction to Matlab & probability
3:19:47 Measuring probability
3:26:53 Generating random values
3:35:40 Birthday paradox
3:43:25 Continuous variables
3:48:00 Mean and variance
3:55:24 Gaussian (normal) distribution
4:03:21 Test for normality
4:10:32 2 sample tests
4:16:28 Multivariate Gaussian

Lecture - 10 Data Acquisition Systems...

Lecture 2 | Image Classification...

Graphical Models 1 - Christopher Bishop - MLSS 201...

Deep Learning for Personalized Search and Recommen...

Deep Learning: Intelligence from Big Data...

SAXually Explicit Images: Data Mining Large Shape ...

Deploying Operating Systems with System Center 201...

Graphical Models 3 - Christopher Bishop - MLSS 201...

11. Introduction to Machine Learning...

Predicting Chaotic Systems with Sparse Data by Dav...

Create Save Update Delete and Search Student Profi...

هل نجحت جولة ولي العهد السعودي الدولية في إخضاع إي...

Agriculture: The Next Machine-Learning Frontier | ...

The Complete MATLAB Course: Beginner to Advanced!...

It turns out that a theory explaining how we might detect parallel universes and prediction for the end of the world was proposed and completed by physicist Stephen Hawking shortly before he died ... &nbsp;. According to reports, the work predicts that the universe would eventually end when stars run out of energy ... ....

Article by WN.Com Correspondent Dallas DarlingIt wasn’t very long ago Republicans were accusing Democrats of either paying a few dollars to the homeless for votes or giving them a pack of cigarettes. But with Donald Trump, it’s obvious he paid $130,000 to an adult-film star in exchange for her silence last October and just before the general election ... Was the payment from his own account – or from a lawyer – or from campaign donations....

And the White House will Monday face uncomfortable new questions about the Trump team's conduct during the 2016 election after Facebook suspended CambridgeAnalytica, a data firm used by President's campaign, over allegations it used the data of 50 million users of the social media site without their permission. Trump's major agenda push this week will highlight his plan to tackle the opioid crisis ... Read More ...CNNMoney ... JUST WATCHED ... ....

Using e-cigarettes may lead to an accumulation of fat in the liver, a study of mice exposed to the devices suggests. “The popularity of electronic cigarettes has been rapidly increasing in part because of advertisements that they are safer than conventional cigarettes ... Friedman of Charles R. Drew University of Medicine and Science in Los Angeles, California ... Circadian rhythm dysfunction is known to accelerate liver disease....

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The social media giant said approximately 270,000 people had downloaded an app developed by University of Cambridge psychology professor Aleksandr Kogan, who it said “lied” and violated its policy by gathering user data and passing it on to CambridgeAnalytica... “We would ask people to fill out psychological surveys,” he said, “That app would then harvest their data from Facebook....

This submitted image shows the current design for Facebook's data center facility in Papillion. An expanded version will be 2.6 million square feet and on both sides of Highway 50 just north of Capehart Road... ....

SAN FRANCISCO - Facebook is suspending the Trump-affiliated data analytics firm CambridgeAnalytica, after learning that it failed to delete data that it had taken inappropriately from users of the social network, Facebook said late Friday ... ....

OVERLAND PARK, Kan., March 19, 2018 /PRNewswire/ -- VeriShip, the leader in parcel shipping intelligence, today announced the launch of VeriShip DataBridge (VDB), enabling companies that rely on parcel carriers like UPS and FedEx to close the data gaps between what happens to a package before it is manifested and all that happens after ... To learn more about VeriShip Data Bridge, visit veriship.com/data-bridge....

The Observer reported on Saturday that CambridgeAnalytica acquired 50 million Facebook profiles from a researcher in 2014This appears to have been among the most consequential data breaches in history, with an impact that may rival the breach of financial records from Equifax ...There are questions now over whether the data was destroyed ... The New York Times reported that at least some of the data is still available on the internet....

He knew this would stick in Negan's craw, and it was a joy to watch.Jeffrey Dean Morgan, The Walking Dead" data-image-credit="Gene Page/AMC" data-image-alt-text="Jeffrey Dean Morgan, The Walking Dead" data-image-credit-url="" data-image-target-url="" data-image-title="Jeffrey Dean Morgan, The Walking Dead" ......